PLCs, Power Supplies & Others

The EPRO PR6423/108-110 represents a critical component for industrial condition monitoring. This 8mm eddy current sensor precisely measures vibration and axial position in rotating machinery. Consequently, it enables reliable predictive maintenance for turbines, compressors, and pumps.

Engineered for durability, the sensor maintains stable performance in demanding environments with extreme temperatures and EMI. Furthermore, it seamlessly connects with epro’s monitoring systems, such as the MMS6000 series, to form a complete protection framework. Therefore, operators gain trustworthy data for safeguarding high-value industrial assets.

Core Technical Specifications & Parameters

The PR6423/108-110 delivers performance defined by precise electrical and physical characteristics. For system integration, always refer to the official EPRO (Germany) documentation for final calibration data.

Key Technical Parameters:

• Probe Diameter: 8 mm

• Cable Length: 1.1 meters (1100 mm)

• Linear Measuring Range: 2 mm (±1 mm from electrical zero)

• Typical Sensitivity: -8.0 V/mm

• Output Voltage Range: -2 V to -18 V (across the 2mm range)

• Probe Operating Temperature: -35 °C to +150 °C

• Connector Type: Industrial locking connector

• Power Supply: Provided via compatible epro monitor (e.g., MMS6000)

• Target Material: Conductive metals (requires material-specific calibration)

Design and Operational Principle

This sensor operates on the eddy current principle to ensure non-contact, wear-free measurement. The active probe tip generates a high-frequency electromagnetic field, which induces eddy currents in a conductive target. Subsequently, the system measures the change in the probe’s oscillation amplitude. This change directly correlates to the distance between the probe and the target, providing high-resolution position and vibration data.

Moreover, its robust stainless steel housing and high-quality cable ensure longevity in harsh conditions. The design prioritizes minimal signal drift, which is essential for accurate long-term trend analysis and early fault detection in critical machinery.

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